To achieve true continuous delivery, teams need to leverage end-to-end automation across their deployment pipelines and tooling. Deployment health checks today involve someone pinging the production app and saying "Yep, the app is still up--everything is OK!" In short, availability = success.
In reality, availability doesn't equal success, and the only way to get a true measure of health is to have multiple engineers sniff-test production by grep'ing logs or looking at monitoring charts. It's not unusual for teams to have four or five engineers spending 60 to 120 minutes per deployment making sure everything really is OK. But what if AI/ML could automate this process?
This session will take a look at some of the machine learning techniques you can apply to automate deployment verification and health checks.
You’ve probably written a hundred abstracts in your day, but have you come up with a template that really seems to resonate? Go back through your past webinar inventory and see what events produced the most registrants. Sure – this will vary by topic but what got their attention initially was the description you wrote.
Paint a mental image of the benefits of attending your webinar. Often times this can be summarized in the title of your event. Your prospects may not even make it to the body of the message, so get your point across immediately. Capture their attention, pique their interest, and push them towards the desired action (i.e. signing up for your event). You have to make them focus and you have to do it fast. Using an active voice and bullet points is great way to do this.
Always add key takeaways. Something like this....In this session, you’ll learn about: